The idea of using Machine Learning (ML) with PDEs is not unique and has been explored for several decades [1]}, [2]}. ML approaches are computationally fast, but they fall short in terms of accuracy, generalization to a wide range and out-of-distribution PDE conditions and in their ability to scale to highly-resolved computational grids, when compared to traditional PDE solvers. A few shortcomings of the current methods are outlined below and subsequently are motivations for this work.
